ArticleBiomedical signal processing and control2022
Deep multi-view feature learning for detecting COVID-19 based on chest X-ray images.
Article in Biomedical signal processing and control, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers, 1 of them a synthesis that pooled it.
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4 citing papers in PubMed, 1 synthesis or guideline pooled it.
- A Comprehensive Review of Artificial Intelligence in Prevention and Treatment of COVID-19 Pandemic.Frontiers in genetics · 2022Pooled it
- EVAE-Net: An Ensemble Variational Autoencoder Deep Learning Network for COVID-19 Classification Based on Chest X-ray Images.Diagnostics (Basel, Switzerland) · 2022Article
- An agent-based transmission model of COVID-19 for re-opening policy design.Computers in biology and medicine · 2022Article
- A Novel Method for COVID-19 Detection Based on DCNNs and Hierarchical Structure.Computational and mathematical methods in medicine · 2022Article
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1 author.
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Abstract
aimCOVID-19 is a pandemic infectious disease which has influenced the life and health of many communities since December 2019. Due to the rapid worldwide spread of this highly contagious disease, making its early detection with high accuracy important for breaking the chain of transition. X-ray images of COVID-19 patients, reveal specific abnormalities associated with this disease.
methodsIn this study, a multi-view feature learning method for detecting COVID-19 based on chest X-ray images is presented. This method provides a framework for exploiting the multiple types of deep features, which is able to preserve both the correlative and the complementary information, and achieve accurate detection at the classification phase. Deep features are extracted using pre-trained deep CNN models of AlexNet, GoogleNet, ResNet50, SqueezeNet, and VGG19. The learned feature representation of X-ray images are then classified using ELM.
resultsThe experiments show that our method achieves accuracy scores of 100%, 99.82%, and 99.82% in detecting three classes of COVID-19, normal, and pneumonia, respectively. The sensitivities of three classes are 100%, 100%, and 99.45%, respectively. The specificities of three classes are 100%, 99.73%, and 100%, respectively. The precision values of three classes are 100%, 99.45%, and 100%, respectively. The F-scores of three classes are 100%, 99.73%, and 99.72%, respectively. The overall accuracy score of our method is 99.82%.
conclusionsThe results demonstrate the effectiveness of our method in detecting COVID-19 cases and can therefore assist experts in early diagnosis based on X-ray images.
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